Overview

Dataset statistics

Number of variables32
Number of observations85895
Missing cells556929
Missing cells (%)20.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 MiB
Average record size in memory256.0 B

Variable types

Categorical20
Numeric11
Boolean1

Alerts

ID has a high cardinality: 85895 distinct values High cardinality
LEGAL NAME has a high cardinality: 38744 distinct values High cardinality
DOING BUSINESS AS NAME has a high cardinality: 41686 distinct values High cardinality
ADDRESS has a high cardinality: 42146 distinct values High cardinality
CITY has a high cardinality: 1223 distinct values High cardinality
STATE has a high cardinality: 54 distinct values High cardinality
ZIP CODE has a high cardinality: 1710 distinct values High cardinality
WARD PRECINCT has a high cardinality: 2383 distinct values High cardinality
LICENSE DESCRIPTION has a high cardinality: 106 distinct values High cardinality
APPLICATION CREATED DATE has a high cardinality: 2899 distinct values High cardinality
APPLICATION REQUIREMENTS COMPLETE has a high cardinality: 3078 distinct values High cardinality
PAYMENT DATE has a high cardinality: 4356 distinct values High cardinality
LICENSE TERM START DATE has a high cardinality: 2825 distinct values High cardinality
LICENSE TERM EXPIRATION DATE has a high cardinality: 297 distinct values High cardinality
LICENSE APPROVED FOR ISSUANCE has a high cardinality: 4223 distinct values High cardinality
DATE ISSUED has a high cardinality: 3498 distinct values High cardinality
LICENSE STATUS CHANGE DATE has a high cardinality: 3152 distinct values High cardinality
LOCATION has a high cardinality: 21388 distinct values High cardinality
LICENSE ID is highly correlated with LICENSE NUMBERHigh correlation
ACCOUNT NUMBER is highly correlated with LICENSE NUMBERHigh correlation
WARD is highly correlated with POLICE DISTRICT and 1 other fieldsHigh correlation
POLICE DISTRICT is highly correlated with WARD and 1 other fieldsHigh correlation
LICENSE NUMBER is highly correlated with LICENSE ID and 1 other fieldsHigh correlation
LATITUDE is highly correlated with WARD and 1 other fieldsHigh correlation
ACCOUNT NUMBER is highly correlated with LICENSE NUMBERHigh correlation
WARD is highly correlated with LATITUDEHigh correlation
POLICE DISTRICT is highly correlated with LATITUDEHigh correlation
LICENSE NUMBER is highly correlated with ACCOUNT NUMBERHigh correlation
LATITUDE is highly correlated with WARD and 1 other fieldsHigh correlation
ACCOUNT NUMBER is highly correlated with LICENSE NUMBERHigh correlation
WARD is highly correlated with LATITUDEHigh correlation
POLICE DISTRICT is highly correlated with LATITUDEHigh correlation
LICENSE NUMBER is highly correlated with ACCOUNT NUMBERHigh correlation
LATITUDE is highly correlated with WARD and 1 other fieldsHigh correlation
LICENSE ID is highly correlated with ACCOUNT NUMBER and 1 other fieldsHigh correlation
ACCOUNT NUMBER is highly correlated with LICENSE ID and 1 other fieldsHigh correlation
WARD is highly correlated with POLICE DISTRICT and 3 other fieldsHigh correlation
PRECINCT is highly correlated with LICENSE STATUSHigh correlation
POLICE DISTRICT is highly correlated with WARD and 2 other fieldsHigh correlation
LICENSE NUMBER is highly correlated with LICENSE ID and 1 other fieldsHigh correlation
SSA is highly correlated with WARD and 3 other fieldsHigh correlation
LATITUDE is highly correlated with WARD and 3 other fieldsHigh correlation
LONGITUDE is highly correlated with WARD and 2 other fieldsHigh correlation
LICENSE STATUS is highly correlated with PRECINCTHigh correlation
WARD has 49701 (57.9%) missing values Missing
PRECINCT has 56701 (66.0%) missing values Missing
WARD PRECINCT has 49700 (57.9%) missing values Missing
POLICE DISTRICT has 54012 (62.9%) missing values Missing
APPLICATION CREATED DATE has 64660 (75.3%) missing values Missing
PAYMENT DATE has 1289 (1.5%) missing values Missing
LICENSE APPROVED FOR ISSUANCE has 6789 (7.9%) missing values Missing
LICENSE STATUS CHANGE DATE has 55400 (64.5%) missing values Missing
SSA has 76446 (89.0%) missing values Missing
LATITUDE has 47246 (55.0%) missing values Missing
LONGITUDE has 47246 (55.0%) missing values Missing
LOCATION has 47246 (55.0%) missing values Missing
ID is uniformly distributed Uniform
ID has unique values Unique
LICENSE ID has unique values Unique

Reproduction

Analysis started2021-11-13 16:55:37.902570
Analysis finished2021-11-13 16:56:51.565959
Duration1 minute and 13.66 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct85895
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
35342-20020816
 
1
1980206-20090702
 
1
1305775-20030221
 
1
19008-20050216
 
1
1648907-20061116
 
1
Other values (85890)
85890 

Length

Max length16
Median length16
Mean length15.45860644
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85895 ?
Unique (%)100.0%

Sample

1st row35342-20020816
2nd row1358463-20051116
3rd row1980233-20090722
4th row1476582-20040211
5th row1141408-20080516

Common Values

ValueCountFrequency (%)
35342-200208161
 
< 0.1%
1980206-200907021
 
< 0.1%
1305775-200302211
 
< 0.1%
19008-200502161
 
< 0.1%
1648907-200611161
 
< 0.1%
2054042-201209161
 
< 0.1%
78942-200202161
 
< 0.1%
1899451-200804151
 
< 0.1%
73978-200202161
 
< 0.1%
1171106-200208161
 
< 0.1%
Other values (85885)85885
> 99.9%

Length

2021-11-13T22:26:51.917412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35342-200208161
 
< 0.1%
1249744-200903161
 
< 0.1%
1476582-200402111
 
< 0.1%
1141408-200805161
 
< 0.1%
2129534-201111291
 
< 0.1%
1275083-200402161
 
< 0.1%
1223497-200308161
 
< 0.1%
1222096-200201241
 
< 0.1%
1197714-200208091
 
< 0.1%
1170778-200702161
 
< 0.1%
Other values (85885)85885
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LICENSE ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct85895
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1751302.855
Minimum30793
Maximum2456551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:52.248808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30793
5-th percentile1218939.6
Q11469024.5
median1778754
Q32067578.5
95-th percentile2196172.7
Maximum2456551
Range2425758
Interquartile range (IQR)598554

Descriptive statistics

Standard deviation335877.9915
Coefficient of variation (CV)0.1917874972
Kurtosis-0.2276203254
Mean1751302.855
Median Absolute Deviation (MAD)297695
Skewness-0.2802107113
Sum1.504281588 × 1011
Variance1.128140251 × 1011
MonotonicityNot monotonic
2021-11-13T22:26:52.622224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12565931
 
< 0.1%
19802061
 
< 0.1%
13057751
 
< 0.1%
15544561
 
< 0.1%
17583121
 
< 0.1%
21765851
 
< 0.1%
12001821
 
< 0.1%
18994511
 
< 0.1%
12088661
 
< 0.1%
12686671
 
< 0.1%
Other values (85885)85885
> 99.9%
ValueCountFrequency (%)
307931
< 0.1%
417191
< 0.1%
489691
< 0.1%
531181
< 0.1%
531831
< 0.1%
532091
< 0.1%
618741
< 0.1%
628891
< 0.1%
679881
< 0.1%
681131
< 0.1%
ValueCountFrequency (%)
24565511
< 0.1%
24565371
< 0.1%
24564851
< 0.1%
24564291
< 0.1%
24563751
< 0.1%
24563511
< 0.1%
24563351
< 0.1%
24560021
< 0.1%
24559291
< 0.1%
24556781
< 0.1%

ACCOUNT NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38897
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196695.4217
Minimum10
Maximum397444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:53.009846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15383
Q154615
median248313
Q3300243.5
95-th percentile364316.8
Maximum397444
Range397434
Interquartile range (IQR)245628.5

Descriptive statistics

Standard deviation126819.3438
Coefficient of variation (CV)0.6447498506
Kurtosis-1.541581426
Mean196695.4217
Median Absolute Deviation (MAD)95058
Skewness-0.2698652744
Sum1.689515325 × 1010
Variance1.608314595 × 1010
MonotonicityNot monotonic
2021-11-13T22:26:53.379429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65406689
 
0.8%
29087164
 
0.2%
20116894
 
0.1%
6341487
 
0.1%
24320682
 
0.1%
2541575
 
0.1%
14774
 
0.1%
5505074
 
0.1%
1107173
 
0.1%
8532572
 
0.1%
Other values (38887)84411
98.3%
ValueCountFrequency (%)
102
 
< 0.1%
121
 
< 0.1%
135
< 0.1%
334
 
< 0.1%
444
 
< 0.1%
491
 
< 0.1%
502
 
< 0.1%
5410
< 0.1%
572
 
< 0.1%
622
 
< 0.1%
ValueCountFrequency (%)
3974441
< 0.1%
3963711
< 0.1%
3928211
< 0.1%
3925771
< 0.1%
3824941
< 0.1%
3816141
< 0.1%
3781841
< 0.1%
3779381
< 0.1%
3779331
< 0.1%
3779181
< 0.1%

SITE NUMBER
Real number (ℝ≥0)

Distinct177
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.556027708
Minimum1
Maximum417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:53.748554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum417
Range416
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.655706391
Coefficient of variation (CV)3.386389891
Kurtosis221.046802
Mean2.556027708
Median Absolute Deviation (MAD)0
Skewness12.14917818
Sum219550
Variance74.92125313
MonotonicityNot monotonic
2021-11-13T22:26:54.077324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161478
71.6%
213721
 
16.0%
34869
 
5.7%
41431
 
1.7%
5629
 
0.7%
6347
 
0.4%
7296
 
0.3%
8258
 
0.3%
10187
 
0.2%
9183
 
0.2%
Other values (167)2496
 
2.9%
ValueCountFrequency (%)
161478
71.6%
213721
 
16.0%
34869
 
5.7%
41431
 
1.7%
5629
 
0.7%
6347
 
0.4%
7296
 
0.3%
8258
 
0.3%
9183
 
0.2%
10187
 
0.2%
ValueCountFrequency (%)
4171
< 0.1%
3071
< 0.1%
2421
< 0.1%
2371
< 0.1%
2151
< 0.1%
1881
< 0.1%
1871
< 0.1%
1851
< 0.1%
1831
< 0.1%
1812
< 0.1%

LEGAL NAME
Categorical

HIGH CARDINALITY

Distinct38744
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
WASTE MANAGEMENT OF ILLINOIS INC.
 
689
GROOT RECYCLING & WASTE SERVICES, INC.
 
164
SUPREME CATERING COMPANY
 
94
STARBUCKS CORPORATION
 
87
ALLIED WASTE TRANSPORTATION INC.
 
82
Other values (38739)
84779 

Length

Max length110
Median length21
Mean length21.96154607
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25019 ?
Unique (%)29.1%

Sample

1st rowCARMEN CAHUE
2nd rowISLA TROPICAL, INC.
3rd rowDJS REMODELING
4th rowALL-BRY CONSTRUCTION CO.
5th rowMCDONOUGH MECHANICAL SERVICE

Common Values

ValueCountFrequency (%)
WASTE MANAGEMENT OF ILLINOIS INC.689
 
0.8%
GROOT RECYCLING & WASTE SERVICES, INC.164
 
0.2%
SUPREME CATERING COMPANY94
 
0.1%
STARBUCKS CORPORATION87
 
0.1%
ALLIED WASTE TRANSPORTATION INC.82
 
0.1%
ROY STROM REFUSE INC75
 
0.1%
GROOT, INC.74
 
0.1%
WALGREEN CO.74
 
0.1%
FAMILY DOLLAR, INC.73
 
0.1%
AMERICAN DRUG STORES LLC72
 
0.1%
Other values (38734)84411
98.3%

Length

2021-11-13T22:26:54.446449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc35858
 
12.1%
9700
 
3.3%
llc5834
 
2.0%
construction5493
 
1.9%
co3613
 
1.2%
services3055
 
1.0%
a2293
 
0.8%
heating2262
 
0.8%
company2134
 
0.7%
corp2102
 
0.7%
Other values (28417)222852
75.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DOING BUSINESS AS NAME
Categorical

HIGH CARDINALITY

Distinct41686
Distinct (%)48.5%
Missing1
Missing (%)< 0.1%
Memory size671.2 KiB
WASTE MANAGEMENT METRO
 
610
GROOT RECYCLING & WASTE SERVICES, INC.
 
88
ROY STROM REFUSE
 
75
CROWN RECYCLING & WASTE SERVICES, INC.
 
74
SUPREME CATERING, INC
 
67
Other values (41681)
84980 

Length

Max length87
Median length21
Mean length21.53520618
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27812 ?
Unique (%)32.4%

Sample

1st rowCLAUDIA'S BRIDAL SHOP
2nd rowISLA TROPICAL
3rd rowDJS REMODELING, INC.
4th rowALL-BRY CONSTRUCTION CO.
5th rowMCDONOUGH MECHANICAL SERVICE

Common Values

ValueCountFrequency (%)
WASTE MANAGEMENT METRO610
 
0.7%
GROOT RECYCLING & WASTE SERVICES, INC.88
 
0.1%
ROY STROM REFUSE75
 
0.1%
CROWN RECYCLING & WASTE SERVICES, INC.74
 
0.1%
SUPREME CATERING, INC67
 
0.1%
GROOT RECYCLING AND WASTE SERVICES, INC.58
 
0.1%
PHILLIP'S ICE CREAM51
 
0.1%
GROEN WASTE SERVICE50
 
0.1%
LAND AND LAKES DISPOSAL SERVICES49
 
0.1%
JACKSON HEWITT TAX SERVICE42
 
< 0.1%
Other values (41676)84730
98.6%

Length

2021-11-13T22:26:54.812859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc24376
 
8.5%
12538
 
4.4%
construction7283
 
2.5%
co3852
 
1.3%
heating3490
 
1.2%
services3422
 
1.2%
llc3254
 
1.1%
and2604
 
0.9%
service2297
 
0.8%
a2113
 
0.7%
Other values (27622)221483
77.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ADDRESS
Categorical

HIGH CARDINALITY

Distinct42146
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
1500 N HOOKER ST
 
537
2500 LANDMEIER RD
 
147
3800 LARAMIE AVE 2
 
108
1900 MAYWOOD RD
 
74
21900 S CENTRAL
 
55
Other values (42141)
84974 

Length

Max length49
Median length18
Mean length18.62718435
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28096 ?
Unique (%)32.7%

Sample

1st row2625 S CENTRAL PARK AVE 1
2nd row2825 W MONTROSE AVE
3rd row1605 CLAVEY RD 1
4th row8 NORTH TRAIL
5th row4081 JOSEPH DR

Common Values

ValueCountFrequency (%)
1500 N HOOKER ST537
 
0.6%
2500 LANDMEIER RD147
 
0.2%
3800 LARAMIE AVE 2108
 
0.1%
1900 MAYWOOD RD74
 
0.1%
21900 S CENTRAL55
 
0.1%
1201 GREENWOOD AVE55
 
0.1%
13701 S KOSTNER53
 
0.1%
2608 S DAMEN AVE51
 
0.1%
4612 W LAKE ST50
 
0.1%
700 E GRAND AVE49
 
0.1%
Other values (42136)84716
98.6%

Length

2021-11-13T22:26:55.346719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ave29984
 
8.3%
w22499
 
6.2%
st20370
 
5.6%
s16681
 
4.6%
n16516
 
4.6%
1st15499
 
4.3%
19731
 
2.7%
rd8420
 
2.3%
dr6231
 
1.7%
e4667
 
1.3%
Other values (15944)211512
58.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CITY
Categorical

HIGH CARDINALITY

Distinct1223
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
CHICAGO
36744 
CICERO
 
1248
SKOKIE
 
1219
DES PLAINES
 
1047
ELK GROVE
 
752
Other values (1218)
44885 

Length

Max length21
Median length7
Mean length8.371267245
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique337 ?
Unique (%)0.4%

Sample

1st rowCHICAGO
2nd rowCHICAGO
3rd rowHIGHLAND
4th rowLEMONT
5th rowWAUKEGAN

Common Values

ValueCountFrequency (%)
CHICAGO36744
42.8%
CICERO1248
 
1.5%
SKOKIE1219
 
1.4%
DES PLAINES1047
 
1.2%
ELK GROVE752
 
0.9%
GLENVIEW738
 
0.9%
NILES725
 
0.8%
ARLINGTON HEIGHTS664
 
0.8%
NORTHBROOK635
 
0.7%
SOUTH HOLLAND618
 
0.7%
Other values (1213)41505
48.3%

Length

2021-11-13T22:26:55.676863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago37458
35.3%
park5812
 
5.5%
grove2263
 
2.1%
heights1561
 
1.5%
oak1374
 
1.3%
cicero1248
 
1.2%
skokie1219
 
1.1%
des1049
 
1.0%
plaines1047
 
1.0%
hills1014
 
1.0%
Other values (1144)52175
49.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

STATE
Categorical

HIGH CARDINALITY

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
IL
80546 
IN
 
1749
WI
 
547
CA
 
354
MI
 
350
Other values (49)
 
2349

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
IL80546
93.8%
IN1749
 
2.0%
WI547
 
0.6%
CA354
 
0.4%
MI350
 
0.4%
NY218
 
0.3%
OH191
 
0.2%
TX167
 
0.2%
MO165
 
0.2%
MN141
 
0.2%
Other values (44)1467
 
1.7%

Length

2021-11-13T22:26:55.956449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
il80546
93.8%
in1749
 
2.0%
wi547
 
0.6%
ca354
 
0.4%
mi350
 
0.4%
ny218
 
0.3%
oh191
 
0.2%
tx167
 
0.2%
mo165
 
0.2%
mn141
 
0.2%
Other values (44)1467
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ZIP CODE
Categorical

HIGH CARDINALITY

Distinct1710
Distinct (%)2.0%
Missing31
Missing (%)< 0.1%
Memory size671.2 KiB
60618
 
1738
60647
 
1680
60622
 
1411
60639
 
1361
60608
 
1265
Other values (1705)
78409 

Length

Max length5
Median length5
Mean length4.996692444
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique569 ?
Unique (%)0.7%

Sample

1st row60623
2nd row60618
3rd row60035
4th row60439
5th row60087

Common Values

ValueCountFrequency (%)
606181738
 
2.0%
606471680
 
2.0%
606221411
 
1.6%
606391361
 
1.6%
606081265
 
1.5%
606321243
 
1.4%
608041222
 
1.4%
606091222
 
1.4%
606111221
 
1.4%
606231028
 
1.2%
Other values (1700)72473
84.4%

Length

2021-11-13T22:26:56.199578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
606181738
 
2.0%
606471680
 
2.0%
606221411
 
1.6%
606391361
 
1.6%
606081265
 
1.5%
606321243
 
1.4%
608041222
 
1.4%
606091222
 
1.4%
606111221
 
1.4%
606231028
 
1.2%
Other values (1700)72473
84.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WARD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct50
Distinct (%)0.1%
Missing49701
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean28.52892745
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:56.491822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q117
median30
Q342
95-th percentile47
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation13.92170978
Coefficient of variation (CV)0.4879857401
Kurtosis-1.010474819
Mean28.52892745
Median Absolute Deviation (MAD)12
Skewness-0.3821678344
Sum1032576
Variance193.8140031
MonotonicityNot monotonic
2021-11-13T22:26:56.825275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
424497
 
5.2%
271906
 
2.2%
251431
 
1.7%
351049
 
1.2%
47968
 
1.1%
30930
 
1.1%
33918
 
1.1%
32904
 
1.1%
12900
 
1.0%
2897
 
1.0%
Other values (40)21794
25.4%
(Missing)49701
57.9%
ValueCountFrequency (%)
1702
0.8%
2897
1.0%
3468
0.5%
4473
0.6%
5308
 
0.4%
6384
0.4%
7281
 
0.3%
8470
0.5%
9291
 
0.3%
10649
0.8%
ValueCountFrequency (%)
50592
 
0.7%
49442
 
0.5%
48573
 
0.7%
47968
 
1.1%
46461
 
0.5%
45818
 
1.0%
44875
 
1.0%
43679
 
0.8%
424497
5.2%
41673
 
0.8%

PRECINCT
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct76
Distinct (%)0.3%
Missing56701
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean54.63297253
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:57.193873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median26
Q341
95-th percentile61
Maximum999
Range998
Interquartile range (IQR)29

Descriptive statistics

Standard deviation163.1749551
Coefficient of variation (CV)2.986748617
Kurtosis29.21323813
Mean54.63297253
Median Absolute Deviation (MAD)14
Skewness5.555225013
Sum1594955
Variance26626.06599
MonotonicityNot monotonic
2021-11-13T22:26:57.528113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91566
 
1.8%
999838
 
1.0%
6684
 
0.8%
7675
 
0.8%
14672
 
0.8%
28670
 
0.8%
12653
 
0.8%
21624
 
0.7%
15582
 
0.7%
20575
 
0.7%
Other values (66)21655
 
25.2%
(Missing)56701
66.0%
ValueCountFrequency (%)
1465
 
0.5%
2423
 
0.5%
3480
 
0.6%
4435
 
0.5%
5516
 
0.6%
6684
0.8%
7675
0.8%
8526
 
0.6%
91566
1.8%
10513
 
0.6%
ValueCountFrequency (%)
999838
1.0%
761
 
< 0.1%
746
 
< 0.1%
738
 
< 0.1%
7215
 
< 0.1%
716
 
< 0.1%
7010
 
< 0.1%
6937
 
< 0.1%
68155
 
0.2%
6752
 
0.1%

WARD PRECINCT
Categorical

HIGH CARDINALITY
MISSING

Distinct2383
Distinct (%)6.6%
Missing49700
Missing (%)57.9%
Memory size671.2 KiB
42-
 
1448
42-9
 
1139
27-
 
834
25-
 
468
35-999
 
455
Other values (2378)
31851 

Length

Max length6
Median length5
Mean length4.764746512
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)0.7%

Sample

1st row22-28
2nd row33-23
3rd row25-Jun
4th row35-999
5th rowFeb-37

Common Values

ValueCountFrequency (%)
42-1448
 
1.7%
42-91139
 
1.3%
27-834
 
1.0%
25-468
 
0.5%
35-999455
 
0.5%
42-12276
 
0.3%
11-275
 
0.3%
30-999267
 
0.3%
18-227
 
0.3%
10-222
 
0.3%
Other values (2373)30584
35.6%
(Missing)49700
57.9%

Length

2021-11-13T22:26:57.863213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
421448
 
4.0%
42-91139
 
3.1%
27835
 
2.3%
25468
 
1.3%
35-999455
 
1.3%
42-12276
 
0.8%
11275
 
0.8%
30-999267
 
0.7%
18227
 
0.6%
10222
 
0.6%
Other values (2372)30583
84.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

POLICE DISTRICT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)0.1%
Missing54012
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean13.52981213
Minimum1
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:58.158037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median14
Q319
95-th percentile25
Maximum181
Range180
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.275874386
Coefficient of variation (CV)0.5377661063
Kurtosis23.74873259
Mean13.52981213
Median Absolute Deviation (MAD)5
Skewness1.051476098
Sum431371
Variance52.93834808
MonotonicityNot monotonic
2021-11-13T22:26:58.425227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
183213
 
3.7%
252665
 
3.1%
192485
 
2.9%
12428
 
2.8%
82295
 
2.7%
122133
 
2.5%
172124
 
2.5%
92069
 
2.4%
161800
 
2.1%
141551
 
1.8%
Other values (18)9120
 
10.6%
(Missing)54012
62.9%
ValueCountFrequency (%)
12428
2.8%
2582
 
0.7%
3511
 
0.6%
41108
1.3%
5435
 
0.5%
6703
 
0.8%
7397
 
0.5%
82295
2.7%
92069
2.4%
101461
1.7%
ValueCountFrequency (%)
1811
 
< 0.1%
1613
 
< 0.1%
811
 
< 0.1%
252665
3.1%
241163
1.4%
234
 
< 0.1%
22659
 
0.8%
212
 
< 0.1%
201038
 
1.2%
192485
2.9%

LICENSE CODE
Real number (ℝ≥0)

Distinct106
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1111.73662
Minimum1002
Maximum8340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:58.754267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1010
Q11010
median1010
Q31011
95-th percentile1605
Maximum8340
Range7338
Interquartile range (IQR)1

Descriptive statistics

Standard deviation250.0208724
Coefficient of variation (CV)0.2248921802
Kurtosis168.4954244
Mean1111.73662
Median Absolute Deviation (MAD)0
Skewness7.442521307
Sum95492617
Variance62510.43661
MonotonicityNot monotonic
2021-11-13T22:26:59.091846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101050078
58.3%
101110633
 
12.4%
16042812
 
3.3%
10122809
 
3.3%
10062321
 
2.7%
15251952
 
2.3%
16051889
 
2.2%
16761549
 
1.8%
10081126
 
1.3%
10201087
 
1.3%
Other values (96)9639
 
11.2%
ValueCountFrequency (%)
100217
 
< 0.1%
100422
 
< 0.1%
100576
 
0.1%
10062321
 
2.7%
1007127
 
0.1%
10081126
 
1.3%
1009458
 
0.5%
101050078
58.3%
101110633
 
12.4%
10122809
 
3.3%
ValueCountFrequency (%)
83401
 
< 0.1%
810022
 
< 0.1%
44044
 
< 0.1%
44018
 
< 0.1%
21017
 
< 0.1%
193260
 
0.1%
193186
0.1%
193075
 
0.1%
190026
 
< 0.1%
1842212
0.2%

LICENSE DESCRIPTION
Categorical

HIGH CARDINALITY

Distinct106
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
Limited Business License
50078 
Home Repair
10633 
Peddler, non-food
 
2812
Home Occupation
 
2809
Retail Food Establishment
 
2321
Other values (101)
17242 

Length

Max length60
Median length24
Mean length22.05056173
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowLimited Business License
2nd rowMobile Food Dispenser
3rd rowHome Repair
4th rowLimited Business License
5th rowLimited Business License

Common Values

ValueCountFrequency (%)
Limited Business License50078
58.3%
Home Repair10633
 
12.4%
Peddler, non-food2812
 
3.3%
Home Occupation2809
 
3.3%
Retail Food Establishment2321
 
2.7%
Massage Therapist1952
 
2.3%
Street Performer1889
 
2.2%
Scavenger, Private1549
 
1.8%
Hazardous Materials1126
 
1.3%
Residential Real Estate Developer1087
 
1.3%
Other values (96)9639
 
11.2%

Length

2021-11-13T22:26:59.463493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
license50119
20.2%
business50082
20.2%
limited50078
20.2%
home13442
 
5.4%
repair12492
 
5.0%
peddler3834
 
1.5%
food3570
 
1.4%
non-food3160
 
1.3%
occupation2809
 
1.1%
2584
 
1.0%
Other values (184)55361
22.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LICENSE NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49768
Distinct (%)57.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1228151.153
Minimum129
Maximum2391410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:26:59.792267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile19407.5
Q187109
median1442202
Q31816581
95-th percentile2129325
Maximum2391410
Range2391281
Interquartile range (IQR)1729472

Descriptive statistics

Standard deviation743148.7101
Coefficient of variation (CV)0.6050954788
Kurtosis-0.9916810173
Mean1228151.153
Median Absolute Deviation (MAD)394408
Skewness-0.6524057955
Sum1.054908152 × 1011
Variance5.522700054 × 1011
MonotonicityNot monotonic
2021-11-13T22:27:00.156610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5026013
 
< 0.1%
3394913
 
< 0.1%
2470412
 
< 0.1%
4614912
 
< 0.1%
7750712
 
< 0.1%
112277211
 
< 0.1%
1683311
 
< 0.1%
116717811
 
< 0.1%
5880611
 
< 0.1%
3735911
 
< 0.1%
Other values (49758)85777
99.9%
ValueCountFrequency (%)
1291
< 0.1%
1301
< 0.1%
1321
< 0.1%
1341
< 0.1%
1352
< 0.1%
1361
< 0.1%
1771
< 0.1%
1841
< 0.1%
2541
< 0.1%
2551
< 0.1%
ValueCountFrequency (%)
23914101
< 0.1%
23801071
< 0.1%
23538281
< 0.1%
23499221
< 0.1%
22784621
< 0.1%
22701801
< 0.1%
22629011
< 0.1%
22410601
< 0.1%
22256141
< 0.1%
22224741
< 0.1%

APPLICATION TYPE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
RENEW
61085 
ISSUE
23398 
C_LOC
 
1400
C_CAPA
 
7
C_EXPA
 
5

Length

Max length6
Median length5
Mean length5.000139705
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENEW
2nd rowRENEW
3rd rowISSUE
4th rowISSUE
5th rowRENEW

Common Values

ValueCountFrequency (%)
RENEW61085
71.1%
ISSUE23398
 
27.2%
C_LOC1400
 
1.6%
C_CAPA7
 
< 0.1%
C_EXPA5
 
< 0.1%

Length

2021-11-13T22:27:00.464447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-13T22:27:00.869974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
renew61085
71.1%
issue23398
 
27.2%
c_loc1400
 
1.6%
c_capa7
 
< 0.1%
c_expa5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

APPLICATION CREATED DATE
Categorical

HIGH CARDINALITY
MISSING

Distinct2899
Distinct (%)13.7%
Missing64660
Missing (%)75.3%
Memory size671.2 KiB
2000-12-18T00:00:00
 
166
2002-03-27T00:00:00
 
133
2003-03-25T00:00:00
 
129
2002-09-24T00:00:00
 
55
2005-02-14T00:00:00
 
52
Other values (2894)
20700 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)0.8%

Sample

1st row2009-06-29T00:00:00
2nd row2004-02-10T00:00:00
3rd row2002-01-14T00:00:00
4th row2001-12-13T00:00:00
5th row2010-06-16T00:00:00

Common Values

ValueCountFrequency (%)
2000-12-18T00:00:00166
 
0.2%
2002-03-27T00:00:00133
 
0.2%
2003-03-25T00:00:00129
 
0.2%
2002-09-24T00:00:0055
 
0.1%
2005-02-14T00:00:0052
 
0.1%
2011-02-22T00:00:0037
 
< 0.1%
2005-02-16T00:00:0034
 
< 0.1%
2000-07-06T00:00:0032
 
< 0.1%
2004-03-10T00:00:0032
 
< 0.1%
2003-07-11T00:00:0031
 
< 0.1%
Other values (2889)20534
 
23.9%
(Missing)64660
75.3%

Length

2021-11-13T22:27:01.132020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2000-12-18t00:00:00166
 
0.8%
2002-03-27t00:00:00133
 
0.6%
2003-03-25t00:00:00129
 
0.6%
2002-09-24t00:00:0055
 
0.3%
2005-02-14t00:00:0052
 
0.2%
2011-02-22t00:00:0037
 
0.2%
2005-02-16t00:00:0034
 
0.2%
2000-07-06t00:00:0032
 
0.2%
2004-03-10t00:00:0032
 
0.2%
2003-07-11t00:00:0031
 
0.1%
Other values (2889)20534
96.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

APPLICATION REQUIREMENTS COMPLETE
Categorical

HIGH CARDINALITY

Distinct3078
Distinct (%)3.6%
Missing214
Missing (%)0.2%
Memory size671.2 KiB
2003-12-15T00:00:00
 
4803
2006-12-20T00:00:00
 
4281
2005-12-21T00:00:00
 
4194
2004-12-20T00:00:00
 
4168
2002-12-17T00:00:00
 
3751
Other values (3073)
64484 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique249 ?
Unique (%)0.3%

Sample

1st row2002-06-28T00:00:00
2nd row2005-09-22T00:00:00
3rd row2009-07-22T00:00:00
4th row2004-02-10T00:00:00
5th row2008-03-24T00:00:00

Common Values

ValueCountFrequency (%)
2003-12-15T00:00:004803
 
5.6%
2006-12-20T00:00:004281
 
5.0%
2005-12-21T00:00:004194
 
4.9%
2004-12-20T00:00:004168
 
4.9%
2002-12-17T00:00:003751
 
4.4%
2001-12-20T00:00:002720
 
3.2%
2001-12-21T00:00:00987
 
1.1%
2004-06-30T00:00:00900
 
1.0%
2004-09-27T00:00:00748
 
0.9%
2011-02-15T00:00:00724
 
0.8%
Other values (3068)58405
68.0%

Length

2021-11-13T22:27:01.425773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-12-15t00:00:004803
 
5.6%
2006-12-20t00:00:004281
 
5.0%
2005-12-21t00:00:004194
 
4.9%
2004-12-20t00:00:004168
 
4.9%
2002-12-17t00:00:003751
 
4.4%
2001-12-20t00:00:002720
 
3.2%
2001-12-21t00:00:00987
 
1.2%
2004-06-30t00:00:00900
 
1.1%
2004-09-27t00:00:00748
 
0.9%
2011-02-15t00:00:00724
 
0.8%
Other values (3068)58405
68.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAYMENT DATE
Categorical

HIGH CARDINALITY
MISSING

Distinct4356
Distinct (%)5.1%
Missing1289
Missing (%)1.5%
Memory size671.2 KiB
2006-01-23T00:00:00
 
353
2004-01-20T00:00:00
 
345
2005-01-19T00:00:00
 
329
2004-01-12T00:00:00
 
280
2006-01-30T00:00:00
 
277
Other values (4351)
83022 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique487 ?
Unique (%)0.6%

Sample

1st row2002-08-21T00:00:00
2nd row2005-11-03T00:00:00
3rd row2009-07-22T00:00:00
4th row2004-02-10T00:00:00
5th row2008-06-04T00:00:00

Common Values

ValueCountFrequency (%)
2006-01-23T00:00:00353
 
0.4%
2004-01-20T00:00:00345
 
0.4%
2005-01-19T00:00:00329
 
0.4%
2004-01-12T00:00:00280
 
0.3%
2006-01-30T00:00:00277
 
0.3%
2005-02-07T00:00:00273
 
0.3%
2003-01-13T00:00:00255
 
0.3%
2005-02-14T00:00:00255
 
0.3%
2004-02-13T00:00:00254
 
0.3%
2004-02-17T00:00:00254
 
0.3%
Other values (4346)81731
95.2%
(Missing)1289
 
1.5%

Length

2021-11-13T22:27:01.671788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2006-01-23t00:00:00353
 
0.4%
2004-01-20t00:00:00345
 
0.4%
2005-01-19t00:00:00329
 
0.4%
2004-01-12t00:00:00280
 
0.3%
2006-01-30t00:00:00277
 
0.3%
2005-02-07t00:00:00273
 
0.3%
2003-01-13t00:00:00255
 
0.3%
2005-02-14t00:00:00255
 
0.3%
2004-02-13t00:00:00254
 
0.3%
2004-02-17t00:00:00254
 
0.3%
Other values (4346)81731
96.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
False
85877 
True
 
18
ValueCountFrequency (%)
False85877
> 99.9%
True18
 
< 0.1%
2021-11-13T22:27:01.839107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

LICENSE TERM START DATE
Categorical

HIGH CARDINALITY

Distinct2825
Distinct (%)3.3%
Missing228
Missing (%)0.3%
Memory size671.2 KiB
2004-02-16T00:00:00
 
5146
2007-02-16T00:00:00
 
4464
2006-02-16T00:00:00
 
4432
2005-02-16T00:00:00
 
4359
2003-02-16T00:00:00
 
4182
Other values (2820)
63084 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.1%

Sample

1st row2002-08-16T00:00:00
2nd row2005-11-16T00:00:00
3rd row2009-07-22T00:00:00
4th row2004-02-11T00:00:00
5th row2008-05-16T00:00:00

Common Values

ValueCountFrequency (%)
2004-02-16T00:00:005146
 
6.0%
2007-02-16T00:00:004464
 
5.2%
2006-02-16T00:00:004432
 
5.2%
2005-02-16T00:00:004359
 
5.1%
2003-02-16T00:00:004182
 
4.9%
2002-02-16T00:00:003975
 
4.6%
2004-08-16T00:00:00929
 
1.1%
2004-11-16T00:00:00786
 
0.9%
2011-03-16T00:00:00731
 
0.9%
2003-08-16T00:00:00718
 
0.8%
Other values (2815)55945
65.1%

Length

2021-11-13T22:27:02.039617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2004-02-16t00:00:005146
 
6.0%
2007-02-16t00:00:004464
 
5.2%
2006-02-16t00:00:004432
 
5.2%
2005-02-16t00:00:004359
 
5.1%
2003-02-16t00:00:004182
 
4.9%
2002-02-16t00:00:003975
 
4.6%
2004-08-16t00:00:00929
 
1.1%
2004-11-16t00:00:00786
 
0.9%
2011-03-16t00:00:00731
 
0.9%
2003-08-16t00:00:00718
 
0.8%
Other values (2815)55945
65.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LICENSE TERM EXPIRATION DATE
Categorical

HIGH CARDINALITY

Distinct297
Distinct (%)0.3%
Missing18
Missing (%)< 0.1%
Memory size671.2 KiB
2005-02-15T00:00:00
7162 
2004-02-15T00:00:00
6368 
2006-02-15T00:00:00
6024 
2003-02-15T00:00:00
5947 
2007-02-15T00:00:00
5893 
Other values (292)
54483 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)0.1%

Sample

1st row2003-08-15T00:00:00
2nd row2006-11-15T00:00:00
3rd row2011-07-15T00:00:00
4th row2005-02-15T00:00:00
5th row2010-05-15T00:00:00

Common Values

ValueCountFrequency (%)
2005-02-15T00:00:007162
 
8.3%
2004-02-15T00:00:006368
 
7.4%
2006-02-15T00:00:006024
 
7.0%
2003-02-15T00:00:005947
 
6.9%
2007-02-15T00:00:005893
 
6.9%
2013-04-15T00:00:001139
 
1.3%
2013-03-15T00:00:001036
 
1.2%
2005-11-15T00:00:00974
 
1.1%
2013-05-15T00:00:00963
 
1.1%
2013-11-15T00:00:00944
 
1.1%
Other values (287)49427
57.5%

Length

2021-11-13T22:27:02.289718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2005-02-15t00:00:007162
 
8.3%
2004-02-15t00:00:006368
 
7.4%
2006-02-15t00:00:006024
 
7.0%
2003-02-15t00:00:005947
 
6.9%
2007-02-15t00:00:005893
 
6.9%
2013-04-15t00:00:001139
 
1.3%
2013-03-15t00:00:001036
 
1.2%
2005-11-15t00:00:00974
 
1.1%
2013-05-15t00:00:00963
 
1.1%
2013-11-15t00:00:00944
 
1.1%
Other values (287)49427
57.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LICENSE APPROVED FOR ISSUANCE
Categorical

HIGH CARDINALITY
MISSING

Distinct4223
Distinct (%)5.3%
Missing6789
Missing (%)7.9%
Memory size671.2 KiB
2003-12-22T00:00:00
 
4833
2006-01-23T00:00:00
 
320
2005-01-19T00:00:00
 
294
2006-01-30T00:00:00
 
243
2003-01-13T00:00:00
 
221
Other values (4218)
73195 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419 ?
Unique (%)0.5%

Sample

1st row2002-08-21T00:00:00
2nd row2006-04-05T00:00:00
3rd row2009-07-22T00:00:00
4th row2004-02-10T00:00:00
5th row2008-06-04T00:00:00

Common Values

ValueCountFrequency (%)
2003-12-22T00:00:004833
 
5.6%
2006-01-23T00:00:00320
 
0.4%
2005-01-19T00:00:00294
 
0.3%
2006-01-30T00:00:00243
 
0.3%
2003-01-13T00:00:00221
 
0.3%
2005-02-07T00:00:00211
 
0.2%
2005-02-14T00:00:00208
 
0.2%
2007-01-16T00:00:00201
 
0.2%
2006-02-23T00:00:00199
 
0.2%
2003-01-21T00:00:00190
 
0.2%
Other values (4213)72186
84.0%
(Missing)6789
 
7.9%

Length

2021-11-13T22:27:02.526733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-12-22t00:00:004833
 
6.1%
2006-01-23t00:00:00320
 
0.4%
2005-01-19t00:00:00294
 
0.4%
2006-01-30t00:00:00243
 
0.3%
2003-01-13t00:00:00221
 
0.3%
2005-02-07t00:00:00211
 
0.3%
2005-02-14t00:00:00208
 
0.3%
2007-01-16t00:00:00201
 
0.3%
2006-02-23t00:00:00199
 
0.3%
2003-01-21t00:00:00190
 
0.2%
Other values (4213)72186
91.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DATE ISSUED
Categorical

HIGH CARDINALITY

Distinct3498
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
2003-12-22T00:00:00
 
566
2003-02-21T00:00:00
 
407
2004-01-21T00:00:00
 
365
2006-01-24T00:00:00
 
311
2005-01-20T00:00:00
 
274
Other values (3493)
83972 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.1%

Sample

1st row2006-04-11T00:00:00
2nd row2006-06-12T00:00:00
3rd row2009-07-22T00:00:00
4th row2004-02-11T00:00:00
5th row2008-06-05T00:00:00

Common Values

ValueCountFrequency (%)
2003-12-22T00:00:00566
 
0.7%
2003-02-21T00:00:00407
 
0.5%
2004-01-21T00:00:00365
 
0.4%
2006-01-24T00:00:00311
 
0.4%
2005-01-20T00:00:00274
 
0.3%
2004-01-13T00:00:00245
 
0.3%
2006-01-31T00:00:00239
 
0.3%
2002-01-22T00:00:00231
 
0.3%
2004-02-17T00:00:00218
 
0.3%
2005-02-08T00:00:00208
 
0.2%
Other values (3488)82831
96.4%

Length

2021-11-13T22:27:02.770880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-12-22t00:00:00566
 
0.7%
2003-02-21t00:00:00407
 
0.5%
2004-01-21t00:00:00365
 
0.4%
2006-01-24t00:00:00311
 
0.4%
2005-01-20t00:00:00274
 
0.3%
2004-01-13t00:00:00245
 
0.3%
2006-01-31t00:00:00239
 
0.3%
2002-01-22t00:00:00231
 
0.3%
2004-02-17t00:00:00218
 
0.3%
2005-02-08t00:00:00208
 
0.2%
Other values (3488)82831
96.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LICENSE STATUS CHANGE DATE
Categorical

HIGH CARDINALITY
MISSING

Distinct3152
Distinct (%)10.3%
Missing55400
Missing (%)64.5%
Memory size671.2 KiB
2012-12-29T00:00:00
10060 
2005-01-03T00:00:00
 
1069
2012-12-31T00:00:00
 
770
2003-04-02T00:00:00
 
358
2012-08-06T00:00:00
 
342
Other values (3147)
17896 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique477 ?
Unique (%)1.6%

Sample

1st row2006-06-15T00:00:00
2nd row2004-05-04T00:00:00
3rd row2007-03-09T00:00:00
4th row2008-07-28T00:00:00
5th row2012-08-07T00:00:00

Common Values

ValueCountFrequency (%)
2012-12-29T00:00:0010060
 
11.7%
2005-01-03T00:00:001069
 
1.2%
2012-12-31T00:00:00770
 
0.9%
2003-04-02T00:00:00358
 
0.4%
2012-08-06T00:00:00342
 
0.4%
2013-07-01T00:00:00297
 
0.3%
2012-08-03T00:00:00265
 
0.3%
2012-08-07T00:00:00184
 
0.2%
2012-08-31T00:00:00160
 
0.2%
2012-04-10T00:00:00130
 
0.2%
Other values (3142)16860
 
19.6%
(Missing)55400
64.5%

Length

2021-11-13T22:27:03.032490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-12-29t00:00:0010060
33.0%
2005-01-03t00:00:001069
 
3.5%
2012-12-31t00:00:00770
 
2.5%
2003-04-02t00:00:00358
 
1.2%
2012-08-06t00:00:00342
 
1.1%
2013-07-01t00:00:00297
 
1.0%
2012-08-03t00:00:00265
 
0.9%
2012-08-07t00:00:00184
 
0.6%
2012-08-31t00:00:00160
 
0.5%
2012-04-10t00:00:00130
 
0.4%
Other values (3142)16860
55.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SSA
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct53
Distinct (%)0.6%
Missing76446
Missing (%)89.0%
Infinite0
Infinite (%)0.0%
Mean29.15207959
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:27:03.342578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q117
median28
Q340
95-th percentile60
Maximum69
Range68
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.41989791
Coefficient of variation (CV)0.5975524956
Kurtosis-0.7530328653
Mean29.15207959
Median Absolute Deviation (MAD)11
Skewness0.231884087
Sum275458
Variance303.4528431
MonotonicityNot monotonic
2021-11-13T22:27:03.671968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33542
 
0.6%
3463
 
0.5%
60458
 
0.5%
34378
 
0.4%
8374
 
0.4%
1365
 
0.4%
27358
 
0.4%
17357
 
0.4%
13353
 
0.4%
25330
 
0.4%
Other values (43)5471
 
6.4%
(Missing)76446
89.0%
ValueCountFrequency (%)
1365
0.4%
2113
 
0.1%
3463
0.5%
463
 
0.1%
588
 
0.1%
759
 
0.1%
8374
0.4%
10262
0.3%
13353
0.4%
14108
 
0.1%
ValueCountFrequency (%)
6975
 
0.1%
6410
 
< 0.1%
6364
 
0.1%
6240
 
< 0.1%
61100
 
0.1%
60458
0.5%
59175
 
0.2%
5650
 
0.1%
5547
 
0.1%
5454
 
0.1%

LATITUDE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21373
Distinct (%)55.3%
Missing47246
Missing (%)55.0%
Infinite0
Infinite (%)0.0%
Mean41.88095213
Minimum41.64469419
Maximum42.02266027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size671.2 KiB
2021-11-13T22:27:03.980761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum41.64469419
5-th percentile41.72724059
Q141.83431211
median41.89267922
Q341.93862431
95-th percentile41.99230124
Maximum42.02266027
Range0.37796608
Interquartile range (IQR)0.1043122

Descriptive statistics

Standard deviation0.07961676768
Coefficient of variation (CV)0.001901025732
Kurtosis-0.1688601576
Mean41.88095213
Median Absolute Deviation (MAD)0.0479852
Skewness-0.6701842091
Sum1618656.919
Variance0.006338829696
MonotonicityNot monotonic
2021-11-13T22:27:04.375592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.90872856538
 
0.6%
41.85761689230
 
0.3%
41.89224916141
 
0.2%
41.88807428123
 
0.1%
41.8194414397
 
0.1%
41.7546601286
 
0.1%
41.8900013471
 
0.1%
41.8445114767
 
0.1%
41.8528716965
 
0.1%
41.8920941464
 
0.1%
Other values (21363)37167
43.3%
(Missing)47246
55.0%
ValueCountFrequency (%)
41.644694191
 
< 0.1%
41.644715441
 
< 0.1%
41.644716521
 
< 0.1%
41.644716558
< 0.1%
41.644716629
< 0.1%
41.644721345
< 0.1%
41.644746179
< 0.1%
41.644798071
 
< 0.1%
41.645667481
 
< 0.1%
41.646370821
 
< 0.1%
ValueCountFrequency (%)
42.022660271
< 0.1%
42.02248021
< 0.1%
42.02212321
< 0.1%
42.022017321
< 0.1%
42.021448531
< 0.1%
42.021317651
< 0.1%
42.021261681
< 0.1%
42.020087321
< 0.1%
42.020026191
< 0.1%
42.019866881
< 0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct21380
Distinct (%)55.3%
Missing47246
Missing (%)55.0%
Infinite0
Infinite (%)0.0%
Mean-87.68116527
Minimum-87.915285
Maximum-87.5258717
Zeros0
Zeros (%)0.0%
Negative38649
Negative (%)45.0%
Memory size671.2 KiB
2021-11-13T22:27:04.790196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-87.915285
5-th percentile-87.77860152
Q1-87.71915015
median-87.67332082
Q3-87.63950931
95-th percentile-87.60489691
Maximum-87.5258717
Range0.3894133
Interquartile range (IQR)0.07964084

Descriptive statistics

Standard deviation0.05624772088
Coefficient of variation (CV)-0.0006415028895
Kurtosis0.5405340645
Mean-87.68116527
Median Absolute Deviation (MAD)0.03818322
Skewness-0.4623129673
Sum-3388789.357
Variance0.003163806104
MonotonicityNot monotonic
2021-11-13T22:27:05.178867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.65472645538
 
0.6%
-87.63607109230
 
0.3%
-87.60951805141
 
0.2%
-87.6349552123
 
0.1%
-87.6653755597
 
0.1%
-87.7413847686
 
0.1%
-87.6214386971
 
0.1%
-87.6757880467
 
0.1%
-87.6405636665
 
0.1%
-87.6115698864
 
0.1%
Other values (21370)37167
43.3%
(Missing)47246
55.0%
ValueCountFrequency (%)
-87.91528511
 
< 0.1%
-87.9145338312
 
< 0.1%
-87.914526851
 
< 0.1%
-87.914468516
 
< 0.1%
-87.914435859
 
< 0.1%
-87.9144284448
0.1%
-87.902322364
 
< 0.1%
-87.894213192
 
< 0.1%
-87.891319424
 
< 0.1%
-87.885823022
 
< 0.1%
ValueCountFrequency (%)
-87.52587171
< 0.1%
-87.526635731
< 0.1%
-87.526665381
< 0.1%
-87.526822791
< 0.1%
-87.526868041
< 0.1%
-87.527160062
< 0.1%
-87.5275932
< 0.1%
-87.527704012
< 0.1%
-87.527819341
< 0.1%
-87.528090391
< 0.1%

LOCATION
Categorical

HIGH CARDINALITY
MISSING

Distinct21388
Distinct (%)55.3%
Missing47246
Missing (%)55.0%
Memory size671.2 KiB
{'latitude': '41.90872856223717', 'longitude': '-87.6547264445884', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
 
538
{'latitude': '41.85761688731556', 'longitude': '-87.63607109247158', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
 
230
{'latitude': '41.892249163400116', 'longitude': '-87.60951804879336', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
 
141
{'latitude': '41.8880742810662', 'longitude': '-87.63495520292739', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
 
123
{'latitude': '41.81944142629576', 'longitude': '-87.6653755449091', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
 
97
Other values (21383)
37520 

Length

Max length159
Median length158
Mean length158.0508939
Min length153

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15280 ?
Unique (%)39.5%

Sample

1st row{'latitude': '41.843612879431845', 'longitude': '-87.71461847216574', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
2nd row{'latitude': '41.96113244107215', 'longitude': '-87.69962604438346', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
3rd row{'latitude': '41.856222490735874', 'longitude': '-87.6390323692093', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
4th row{'latitude': '41.92437507335029', 'longitude': '-87.72670493056832', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}
5th row{'latitude': '41.9232444111139', 'longitude': '-87.66645762597676', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}

Common Values

ValueCountFrequency (%)
{'latitude': '41.90872856223717', 'longitude': '-87.6547264445884', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}538
 
0.6%
{'latitude': '41.85761688731556', 'longitude': '-87.63607109247158', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}230
 
0.3%
{'latitude': '41.892249163400116', 'longitude': '-87.60951804879336', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}141
 
0.2%
{'latitude': '41.8880742810662', 'longitude': '-87.63495520292739', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}123
 
0.1%
{'latitude': '41.81944142629576', 'longitude': '-87.6653755449091', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}97
 
0.1%
{'latitude': '41.75466012439374', 'longitude': '-87.74138475860521', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}86
 
0.1%
{'latitude': '41.890001335214684', 'longitude': '-87.62143868830125', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}71
 
0.1%
{'latitude': '41.844511469065175', 'longitude': '-87.67578803912177', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}67
 
0.1%
{'latitude': '41.852871687739125', 'longitude': '-87.64056365490387', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}65
 
0.1%
{'latitude': '41.892094136861786', 'longitude': '-87.61156988394656', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}'}64
 
0.1%
Other values (21378)37167
43.3%
(Missing)47246
55.0%

Length

2021-11-13T22:27:05.512791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
latitude38649
12.5%
human_address38649
12.5%
longitude38649
12.5%
address":"","city":"","state":"","zip38649
12.5%
needs_recoding38649
12.5%
false38649
12.5%
41.90872856223717538
 
0.2%
87.6547264445884538
 
0.2%
41.85761688731556230
 
0.1%
87.63607109247158230
 
0.1%
Other values (42772)75762
24.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LICENSE STATUS
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size671.2 KiB
AAI
55400 
AAC
30200 
REV
 
290
REA
 
3
INQ
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAAI
2nd rowAAC
3rd rowAAI
4th rowAAI
5th rowAAI

Common Values

ValueCountFrequency (%)
AAI55400
64.5%
AAC30200
35.2%
REV290
 
0.3%
REA3
 
< 0.1%
INQ2
 
< 0.1%

Length

2021-11-13T22:27:05.806637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-13T22:27:05.975557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
aai55400
64.5%
aac30200
35.2%
rev290
 
0.3%
rea3
 
< 0.1%
inq2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-13T22:26:39.787551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:02.332608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:06.176148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:09.938236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:13.426950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:16.682306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:20.129186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:23.886766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:28.105683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:32.138778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:35.932283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:40.112785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:02.728428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:06.541364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:10.441197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:13.718158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:16.966389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:20.430054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:24.276887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:28.615304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:32.414144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:36.288297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:40.454025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:03.204309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:06.913721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:10.769774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:14.035619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:17.298306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:20.757726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:24.726711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:29.194008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:32.737765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:36.650119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:40.745610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:03.493815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:07.223226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:11.021505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:14.317804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:17.572781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:21.101228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:25.026208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:29.488704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:33.121989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:37.006916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:41.087933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:03.835700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:07.521610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:11.305785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:14.592746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:17.849319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:21.400077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:25.325764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:29.787555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:33.436790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:37.324124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:41.404100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:04.120386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:07.823670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:11.603389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:14.856938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:18.301757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:21.779806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:25.691059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:30.146191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:33.792488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:37.641978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:41.761313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:04.475179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:08.216540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:11.920810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:15.165920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:18.614897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:22.086136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:26.043531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:30.488844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:34.102076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:37.964648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:42.075776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:04.822592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:08.589934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:12.238009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:15.459470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:18.911545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:22.432624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:26.589710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:30.830918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:34.435413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:38.306942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:42.409857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:05.125706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:08.898277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:12.511520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:15.734070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:19.189105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:22.743662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:27.007780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:31.125646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:34.753446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:38.602886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:42.725213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:05.459877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:09.224884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:12.818961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:16.032776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:19.497445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:23.104924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:27.365196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:31.447592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:35.137904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:38.943064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:43.053133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:05.780381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:09.563790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:13.127674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:16.342512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:19.817545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:23.471090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:27.720231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:31.809531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:35.634179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-13T22:26:39.330531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-13T22:27:06.223321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-13T22:27:06.861537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-13T22:27:07.311683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-13T22:27:07.705623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-13T22:27:08.053573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-13T22:26:44.129344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-13T22:26:47.003578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-13T22:26:49.493534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-13T22:26:50.601021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

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